The field of the present disclosure relates generally to inspection of an object and, more specifically, to detecting potential anomalies of the object by inspecting a three-dimensional model of the object generated using structure from motion range imaging techniques.
At least some known aircraft require daily inspections to detect potential damage and/or other maintenance issues. Such inspections are typically conducted manually by a maintenance worker or other personnel when the aircraft is not in service. For example, in some instances, the maintenance worker visually inspects the aircraft with the naked eye while physically moving around the aircraft. However, finding and accurately determining locations of potential damage via visual inspection on large commercial aircraft, for example, can be a time-consuming and laborious task susceptible to human error.
Several attempts have been made to automate visual inspection techniques for known aircraft. At least one known method includes capturing two-dimensional images of the aircraft taken at different times, and comparing the images to determine variations therebetween. However, it may be difficult to accurately determine variations between the images when they are taken from different angles and/or distances, for example. Another known method includes capturing a two-dimensional image of the aircraft, and comparing the image to a three-dimensional model of the aircraft. However, the dimensions of the three-dimensional model may be unavailable or inaccurate such that a comparison between the three-dimensional model and the image will result in false detection of anomalies.